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An Improved Flower Pollination Algorithm with Three Strategies and Its Applications

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Abstract

The flower pollination algorithm is a recently presented meta-heuristic algorithm, but limited in searching precision and convergence rate when solving some complex problems. In order to enhance its performance, this paper proposes an improved flower pollination algorithm, combined with three strategies, i.e., a new double-direction learning strategy to advance the local searching ability, a new greedy strategy to strengthen the diversity of population and a new dynamic switching probability strategy to balance global and local searching. These strategies can increase searching precision and make solution more accurate. Then 12 standard test functions and two structural design examples are selected to appraise the performance of the newly proposed algorithm. The results show that our new algorithm has outstanding performance, such as high accuracy, fast convergence speed and strong stability on solving some complex optimization problems.

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References

  1. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol 4, pp 1942–1948

  2. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–72

    Google Scholar 

  3. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B (Cybern) 26(1):29–41

    Google Scholar 

  4. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Google Scholar 

  5. Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26(2):69–74

    Google Scholar 

  6. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Google Scholar 

  7. Yang XS, Deb S (2009) Cuckoo search via levy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009 India). pp 210–214. https://doi.org/10.1109/NABIC.2009.5393690

  8. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Comput Knowl Technol 284:65–74

    Google Scholar 

  9. Reynolds RG, Zhu S (2001) Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming. IEEE Trans Syst Man Cybern B Cybern A 31(1):1–18

    Google Scholar 

  10. Yang XS (2012) Flower pollination algorithm for global optimization. Springer, Berlin, pp 240–249

    Google Scholar 

  11. Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192–203

    Google Scholar 

  12. Salgotra R, Singh U (2018) A novel bat flower pollination algorithm for synthesis of linear antenna arrays. Neural Comput Appl 30(7):2269–2282

    Google Scholar 

  13. Zhou Y, Wang R, Luo Q (2016) Elite opposition-based flower pollination algorithm. Neurocomputing 188:294–310

    Google Scholar 

  14. Salgotra R, Singh U (2017) Application of mutation operators to flower pollination algorithm. Expert Syst Appl 79:112–129

    Google Scholar 

  15. Draa A (2015) On the performances of the flower pollination algorithm—qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Google Scholar 

  16. Wang R, Zhou Y (2014) Flower pollination algorithm with dimension by dimension improvement. Math Probl Eng 2014(4):1–9

    Google Scholar 

  17. Zhou Y, Zhang S, Luo Q, Wen C (2016) Using flower pollination algorithm and atomic potential function for shape matching. Neural Comput Appl 29(6):21–40

    Google Scholar 

  18. Yang XS, Karamanoglu M, He X (2013) Multi-objective flower algorithm for optimization. Procedia Comput Sci 18(1):861–868

    Google Scholar 

  19. Ram JP, Babu TS, Dragicevic T, Rajasekar N (2017) A new hybrid bee pollinator flower pollination algorithm for solar pv parameter estimation. Energy Convers Manag 135:463–476

    Google Scholar 

  20. Xu S, Wang Y (2017) Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers Manag 144(15):53–68

    Google Scholar 

  21. Sayed AEF, Nabil E, Badr A (2016) A binary clonal flower pollination algorithm for feature selection. Pattern Recognit Lett 77:21–27

    Google Scholar 

  22. Abdelaziz AY, Ali ES, Elazim SMA (2016) Flower pollination algorithm to solve combined economic and emission dispatch problems. Eng Sci Technol Int J 19(2):980–990

    Google Scholar 

  23. Pavlyukevich I (2007) Levy flights, non-local search and simulated annealing. J Comput Phys 226(2):1830–1844

    Google Scholar 

  24. Abdel-Basset M, Shawky LA (2018) Flower pollination algorithm: a comprehensive review. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9624-4

  25. Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York

    Google Scholar 

  26. dos Santos Coelho L (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37(2):1676–1683

    Google Scholar 

  27. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Google Scholar 

  28. Kaveh A, Talatahari S (2013) An improved ant colony optimization for constrained engineering design problems. Eng Comput 27(1):155–182

    Google Scholar 

  29. Canayaz M, Karci A (2015) Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems. Appl Intell 44(2):1–15

    Google Scholar 

  30. Du T, Ke X, Liao J, Shen Y (2018) DSLC-FOA: improved fruit fly optimization algorithm for application to structural engineering design optimization problems. Appl Math Model 55:314–339

    Google Scholar 

  31. Mazhoud I, Hadj-Hamou K, Bigeon J, Joyeux P (2013) Particle swarm optimization for solving engineering problems: a new constraint-handling mechanism. Eng Appl Artif Intell 26(4):1263–1273

    Google Scholar 

  32. Long W, Liang X, Huang Y, Chen Y (2014) An effective hybrid cuckoo search algorithm for constrained global optimization. Neural Comput Appl 25(3–4):911–926

    Google Scholar 

  33. Liu J, Wu C, Wu G, Wang X (2015) A novel differential search algorithm and applications for structure design. Appl Math Comput 268(C):246–269

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61374028, 61773172)

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Authors and Affiliations

  1. Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang, 443002, Hubei, China

    Xin Yang & Yanjun Shen

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  1. Xin Yang
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  2. Yanjun Shen
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Correspondence to Yanjun Shen.

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Yang, X., Shen, Y. An Improved Flower Pollination Algorithm with Three Strategies and Its Applications. Neural Process Lett 51, 675–695 (2020). https://doi.org/10.1007/s11063-019-10103-y

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